Spatio-Temporal LLM: Reasoning about Environments and Actions

• URL: http://arxiv.org/abs/2507.05258v1
• Date: Mon, 07 Jul 2025 17:59:55 GMT
• Title: Spatio-Temporal LLM: Reasoning about Environments and Actions
• Authors: Haozhen Zheng, Beitong Tian, Mingyuan Wu, Zhenggang Tang, Klara Nahrstedt, Alex Schwing,
• Abstract summary: We show that MLLMs still struggle to correctly answer prompts that require a holistic-temporal understanding.<n>We develop a "s-temporal LLM" (LLM), a model equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations.
• Score: 6.224087801093545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the significant recent progress of Multimodal Large Language Models (MLLMs), MLLMs still struggle to correctly answer prompts that require a holistic spatio-temporal understanding. Specifically, it is challenging to address prompts that refer to 1) the entirety of an environment that an agent equipped with an MLLM can operate in; and simultaneously also refer to 2) recent actions that just happened and are encoded in a video clip. However, such a holistic spatio-temporal understanding is important for agents operating in the real world. To address this issue, we first develop a framework to collect a large-scale dataset. Using the collected "Reasoning about Environments and Actions" (REA) dataset, we show that recent methods indeed struggle to correctly answer the prompts. To improve, we develop a "spatio-temporal LLM" (ST-LLM), a model equipped with projectors to improve both spatial understanding of an environment and temporal understanding of recent observations. On the collected REA data, we show that the proposed method significantly improves results compared to prior work. Code and data are available at https://zoezheng126.github.io/STLLM-website/.

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